{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iDKAL2G24hSLiRuDJjYzPzMxaqJ7mo7+mv3+RtAPwBDBtI7Z5kqQPALcAp0TEqsEKSZoD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\nc/riLTZG8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f890966a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"np\" and \"plt\" are common aliases for NumPy and Matplotlib, respectively.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# X represents the features of our training data, the diameters of the pizzas.\n",
    "# A scikit-learn convention is to name the matrix of feature vectors X. \n",
    "# Uppercase letters indicate matrices, and lowercase letters indicate vectors.\n",
    "X = np.array([6, 8, 10, 14, 18]).reshape(-1, 1)\n",
    "\n",
    "# y is a vector representing the prices of the pizzas.\n",
    "y = [7, 9, 13, 17.5, 18]\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Pizza price plotted against diameter')\n",
    "plt.xlabel('Diameter in inches')\n",
    "plt.ylabel('Price in dollars')\n",
    "plt.plot(X, y, 'k.')\n",
    "plt.axis([0, 25, 0, 25])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
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   "source": []
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